Sunday, January 17, 2016

Deep learning just got deeper – writing on blackboard for some forms of teaching?

'I think there is a world market for maybe five computers', so
Thomas Watson of IBM NEVER said. It’s just one of many made-up and misattributed quotes (mostly from
Einstein) which pepper slides at education and tech conferences. But in a weird
sort of way this often mocked quote (oh how we laugh) is turning out to be true. The only people
with the computing power to solve the big problems may just be be Google,
Microsoft, Facebook, Amazon and IBM. They bring services to the cloud, power on
tap, making AI a utility, like electricity. Nicholas Carr wrote about this in
The Big Switch, but underestimated the ultimate reach of such cloud services.

Deep Learning

We’re in the Age of Algorithms. They find things for you on
Google, stop porn appearing on Twitter, protect your savings and online
transactions, filter out spam, allow you to use files and share files. The
world of learning is not immune, where there’s 5 levels at which AI currentlyoperates. But it’s Deep Learning by software, that is sprinting ahead at the
moment.

Microsoft – image
recognition

An interesting, but only one case, of deep learning is
visual recognition. Only last month IBM wiped the floor with their image recognition
system. The point, of course, is not to mimic the human eye but to produce
perceptual apparatus that is better – higher fidelity, more range on
electromagnetic spectrum and so on. It’s really the cognitive recognition of
images that matter – that’s the hard bit.

It’s best to see neural networks, not in terms of the meat
brain, but in terms of layers of algorithmic maths. As these layers get deeper
and more complex they can handle more complex tasks with higher degrees of
success. The problem with the layers has been a law of diminishing returns. A
success on one layer gets diminished as it moves through lower levels. The
trick is to ‘preserve’ success by moving success forward on a conditional
basis, only taking it to other 'relevant' layers. Microsoft has done this down to
over 150 layers.

Given the increase in speed and reduction in cost of
processing power, deep learning researchers also run many models and allow the
software to learn through many iterations. Raw experimentation then produces optimised solutions. The resources needed to do this well are mind-blowing,
with all but a few heavyweights excluded. The winners are likely to be those who
have the deep pockets and deep commitment to succeed - these are the big tech companies.

AI passes University
entrance exams

I first
heard about this from Professor Toby Walsh in Berlin, who stated that in
November 2015, an AI programme had passed the entrance exam for Tokyo
University that includes maths, physics, English and history. This was the
Todai Robot Project. Remarkably it had scored a much higher than average score
(53.8% against a national human average of 43.8%), with its highest marks in
maths and history. The point, of course, is NOT to get a piece of software or
robot into a top university. It is to act as the basis for research into the
development of machine intelligence to solve problems.

AI predicts student performance (85%)

Other
researchers, such as the Chris Piech’s team at Stanford and Google, have
developed AI that does detailed analysis on student performance as the student
learns and predicts how they will perform on subsequent problems. Their
approach used 1.4 million student answers to maths problems posed by the Khan
Academy. As the internet and global education projects, such as Khan and MOOCs,
slew off huge amounts of data, we are now in apposition to exploit AI (a neural
network) to be predictive on the basis of an enormous amount of real human
data. We can, in a sense, bypass traditional cognitive psychology and use large
data sets in conjunction with smart sets of algorithms, to diagnose what
students are likely to get right or wrong. More than this, it can tell what
went wrong and why. The accuracy stands, presently at around 85%. This has obvious applications in terms of doing what a teacher can do, assessing and predicting performance, only better.

What’s the point

Why are these three, and many more successes, all so interesting? Well, image recognition, (and speech and other
forms of data) has already revolutionized search, fraud detection and can be
used in online assessment to authenticate students for online exams. Adaptive
learning systems to present personalized learning to each and every student,
according to their measured progress. This gets away from the obvious faults in
one-size-fits-all, linear curricula and teaching. It also allows the system to
track each and every student to a degree that is impractical for real teachers.
This one-to-one diagnosis works in all sorts of other areas of online activity,
such as Google, advertising, Amazon, online dating and Netflix. There is every
reason to suppose that it will work in optimizing learning journeys. The net
results may be faster progression, less dropout, the ability to deliver on
scale and volume, therefore lowering the currently skyrocketing costs in
education. For me, the ultimate goal is to satisfy growing demand in the
developing world, which we will never satisfy using our existing, expensive
methods. The point of projects like Tokai is not that such a piece of
software can pass an exam but that it can do things which graduates think is
their sole domain. If a machine can do a graduate level task in the workplace,
as robots can in factories, then their jobs are under threat. The interesting
point is the degree to which AI and deep learning will result in the erosion of
middle class professions, including teaching. Augmented intelligence and
augmented teaching are already in operation. But the writing is on the
blackboard for other forms of learning and teaching.